Bayesian　finite　element　model　updating　techniques　have　found　widespread　application　in　the　structural　health　monitoring.　The　conventional　Bayesian　modeling　framework　(CBMF)　identifies　the　posterior　distribution　of　structural　parameters　using　data　from　a　single　experiment.　However,　structural　parameters　exhibit　variability　due　to　changes　in　environmental　or　experimental　conditions,　an　aspect　overlooked　by　CBMF.　In　recent　years,　the　hierarchical　Bayesian　modeling　framework　(HBMF)　has　gradually　attracted　attention.　This　framework　incorporates　an　additional　layer　of　hyperparameters　and　utilizes　multiple　sets　of　experimental　data　to　describe　the　variability　in　structural　parameters　caused　by　changes　in　experimental　conditions.　Unfortunately,　the　increase　in　hyperparameters　and　data　size　leads　the　HBMF　to　face　the　dilemma　of　exceedingly　high　computational　costs.　To　address　this　issue,　this　study　proposes　an　improved　hierarchical　Bayesian　modeling　framework　(IHBMF).　To　enhance　the　computational　efficiency　of　IHBMF,　the　importance　sampling　method　is　employed　to　simplify　the　computation　of　the　likelihood　function　for　hyperparameters.　Subsequently,　a　surrogate　model　based　on　arbitrary　polynomial　chaos　expansion　is　introduced　to　further　reduce　the　computational　cost　of　IHBMF.　The　framework＇s　accuracy　and　efficiency　were　validated　through　analysis　of　a　simply　supported　beam　and　a　steel　pedestrian　bridge.　The　results　demonstrate　that　IHBMF　not　only　delivers　a　more　accurate　quantification　of　structural　parameter　uncertainty　than　CBMF　but　also　exhibits　higher　computational　efficiency　compared　to　HBMF,　showcasing　its　superior　capability　in　structural　health　monitoring　applications.　©　2024　Elsevier　Ltd